Affiliations 

  • 1 College of Civil Engineering and Architecture, Zhejiang University, China. Electronic address: yueyi@zju.edu.cn
  • 2 College of Civil Engineering and Architecture, Anzhong Building, Zijingang Campus, Zhejiang University, Zhejiang University, A501, , 866 Yuhangtang Rd, Hangzhou 310058, China. Electronic address: feifeizheng@zju.edu.cn
  • 3 School of Civil, Environmental and Mining Engineering, The University of Adelaide, Australia. Electronic address: holger.maier@adelaide.edu.au
  • 4 Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa 32000, Israel. Electronic address: ostfeld@technion.ac.il
  • 5 Dipartimento di Ingegneria Civile e Architettura, University of Pavia, Via Ferrata 3 Pavia 27100, Italy; School of Civil, Environmental and Mining Engineering, The University of Adelaide, Australia. Electronic address: enrico.creaco@unipv.it
  • 6 KWR Water Research Institute, the Netherlands; Centre for Water Systems, University of Exeter, United Kingdom; Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Malaysia. Electronic address: Dragan.Savic@kwrwater.nl
  • 7 Faculty of Civil Engineering and Geosciences, Delft University of Technology, the Netherlands. Electronic address: J.G.Langeveld@tudelft.nl
  • 8 Faculty of Civil Engineering and Geosciences, Department of Water Management, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands; Centre for Water Systems, University of Exeter, North Park Road, Exeter EX4 4QF, United Kingdom. Electronic address: z.kapelan@tudelft.nl
Water Res, 2021 Sep 01;202:117419.
PMID: 34274902 DOI: 10.1016/j.watres.2021.117419

Abstract

Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.